Method of providing a descriptor for at least one feature of an image and method of matching features

ABSTRACT

A method of providing a descriptor for at least one feature of an image comprises the steps of providing an image captured by a capturing device and extracting at least one feature from the image, and assigning a descriptor to the at least one feature, the descriptor depending on at least one parameter which is indicative of an orientation, wherein the at least one parameter is determined from the orientation of the capturing device measured by a tracking system. The invention also relates to a method of matching features of two or more images.

This application is entitled to the benefit of, and incorporates byreference essential subject matter disclosed in PCT Application No.PCT/EP2010/057461 filed on May 28, 2010, which claims priority to GermanApplication No. 10 2009 049 849.4 filed Oct. 19, 2009.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to a method of providing a descriptor forat least one feature of an image and to a method of matching features oftwo or more images. Moreover, the invention relates to a computerprogram product comprising software code sections for implementing themethod according to the invention.

2. Background Information

Many applications in the field of computer vision require findingcorresponding points or other features in two or more images of the samescene or object under varying viewpoints, possibly with changes inillumination and capturing hardware used. The features can be points, ora set of points (lines, segments, regions in the image or simply a groupof pixels). Example applications include narrow and wide-baseline stereomatching, camera pose estimation, image retrieval, object recognition,and visual search.

For example, Augmented Reality Systems permit the superposition ofcomputer-generated virtual information with visual impressions of a realenvironment. To this end, the visual impressions of the real world, forexample captured by a camera in one or more images, are mixed withvirtual information, e.g., by means of a display device which displaysthe respective image augmented with the virtual information to a user.Spatial registration of virtual information and the real world requiresthe computation of the camera pose (position and orientation) that isusually based on feature correspondences.

A common way, e.g. such as described in David G. Lowe: “DistinctiveImage Features from Scale-Invariant Keypoints”, International Journal ofComputer Vision, 60, 2 (2004), pp. 91-110, to gain such correspondencesis to first extract features or interest points (e.g. at edges, cornersor local extrema) from the individual images that have a highrepeatability. That is, the probability that the same sets of pixelscorresponding to the same physical entities are extracted in differentimages is high. The second step is then to create a descriptor for eachfeature, based on the intensities of its neighborhood pixels, thatenables the comparison and therefore matching of features. The two mainrequirements for a good descriptor are distinctiveness, i.e. differentfeature points result in different descriptors, and invariance to

1) changes in viewing direction, rotation and scale,

2) changes in illumination,

3) image noise.

This is to ensure that the same feature in different images will bedescribed in a similar way with respect to a similarity measure. Toaddress the invariance against rotation, a spatial normalizationtransforms the pixels of the local neighborhood around a feature pointto a normalized coordinate system prior to the construction of thedescriptor.

It is critical to the invariance that this normalization isreproducible. More advanced methods exist, but in the simplest case thenormalization only consists of an in-plane rotation according to thefeature orientation. The orientation is usually defined based on thepixel intensities in the neighborhood of a feature point, e.g. as thedirection of the largest gradient. Ideally the pixels in the normalizedneighborhood of a feature are identical for different images taken withvarying viewing direction, rotation and scale. In practice, they are atleast very similar, cf. FIG. 2.

In FIG. 2, there is shown an exemplary feature point in different scenes21 and 22. In the first column showing the scenes 21 and 22, the samefeature point under two different orientations is shown as feature pointF21 in scene 21 and feature point F22 in scene 22. In a next step theorientation is defined based on the pixel intensities in theneighborhood of the respective feature point F21 and F22, in the presentexample as the direction of the largest gradient (depicted by the whiteline within the respective rectangle). Then, a spatial normalizationtransforms the pixels of the local neighborhood around feature pointsF21 and F22 (in the present case, the pixels within the rectangle) to anormalized coordinate system (depictions 31 and 32 in the second column)prior to the construction of the descriptors d1 and d2 (third column),respectively. As a result, alignment of the descriptors d1, d2 to thelargest gradient results in a very similar normalized neighborhood (asshown in depictions 31 and 32) and, therefore, similar descriptors d1and d2. This property is common among local feature descriptors andreferred to as invariance to rotation. Invariance to scale is usuallyhandled by constructing an image pyramid containing the image atdifferent scales and performing the above on every scale level. Otherapproaches store the scale with every feature descriptor.

A variety of local feature descriptors exist, wherein a good overviewand comparison is given in Krystian Mikolajczyk and Cordelia Schmid, “Aperformance evaluation of local descriptors”, IEEE Transactions onPattern Analysis & Machine Intelligence, 10, 27 (2005), pp. 1615-1630.Most of them are based on the creation of histograms of either intensityvalues of the normalized local neighborhood pixels or of functions ofthem, such as gradients. The final descriptor is expressed as ann-dimensional vector (as shown in FIG. 2 on the right) and can becompared to other descriptors using a similarity measure such as theEuclidian distance.

In FIG. 3, there is shown a standard approach for creating a featuredescriptor. In step S1, an image is captured by a capturing device, e.g.a camera, or loaded from a storage medium. In step S2, feature pointsare extracted from the image and stored in a 2-dimensional description(parameters u, v). In step S3, an orientation assignment is performed asdescribed above with respect to FIG. 2, to add to the parameters u, v anorientation angle a. Thereafter, a neighborhood normalization step S4 isperformed, as described above with respect to FIG. 2 to gain normalizedneighborhood pixel intensities i[ ]. In the final step S5, a featuredescriptor in the form of a descriptor vector d[ ] is created for therespective extracted feature as a function of the normalizedneighborhood pixel intensities i[ ]. Approaches exist that may assignmultiple orientation angles to a feature in step S3 and consequentlycarry out the steps S4 and S5 for each orientation resulting in onedescriptor per assigned orientation.

A major limitation of the standard approaches as described above is thatwhile invariance to rotation is clearly an important characteristic oflocal feature descriptors in many applications, it may however lead tomismatches when images contain multiple congruent or near-congruentfeatures, as for instance the four corners of a symmetric window orindividual dartboard sections.

In an example, as shown in FIG. 1, a real object 3 which is in thepresent example a building having a window 4, is captured by a mobiledevice 1 having a camera on the rear side (not shown). For instance, themobile device 1 may be a mobile phone having a camera and an opticallens on the rear side for capturing an image of the window 4. On thedisplay 6 of the mobile device 1, the window 4 is depicted as shown. Animage processing method extracts features from the displayed image, forexample the features F1 to F4 representing the four corners of thewindow that can be considered as prominent features of the window, andcreates a feature descriptor for each of the features F1 to F4. Due toinvariance to rotation, as schematically illustrated in FIG. 1 in theleft column, an ideal local feature descriptor would describe thesefeatures F1 to F4 in exactly the same way making them indistinguishable,as illustrated by the extracted features F1 to F4 depicted in anormalized coordinate system in the left column.

In a real word setting with camera noise and aliasing, the descriptorswill not be identical but very similar and therefore virtuallyindistinguishable. Consequently, the probability of mismatches is veryhigh for such scenes which may result in a complete failure of anysystem relying upon such local feature descriptors.

A variety of approaches exist that assume all camera images to be takenin an upright orientation and therefore do not need to deal with theorientation. Here congruent or near-congruent features in differentorientations can easily be distinguished from each other, but the fieldof possible applications is very limited since the camera orientation isheavily constraint.

Therefore, it would be beneficial to have a method of providing adescriptor for at least one feature of an image, wherein the descriptoris provided in a way that the probability of mismatches due to congruentor near-congruent features in different orientations on a static objector scene in a feature matching process may be reduced withoutconstraining the orientation or movement of the capturing device orwithout needing prior knowledge on the orientation or movement of thecapturing device.

SUMMARY OF THE INVENTION

In a first aspect, there is provided a method of providing a descriptorfor at least one feature of an image according to the features of claim1, Further, in another aspect, there is provided a method of matchingfeatures of two or more images according to claim 12. The invention isalso concerned with a computer program product comprising software codesections for implementing such methods according to claim 16.Particularly, in a first aspect, there is provided a method of providinga descriptor for at least one feature of an image, comprising the stepsof providing an image captured by a capturing device and extracting atleast one feature from the image, and assigning a descriptor to the atleast one feature, the descriptor depending on at least one parameterwhich is indicative of an orientation, wherein the at least oneparameter is determined from an absolute or relative orientation of thecapturing device measured by a tracking system. Particularly, thetracking system in the context of this invention determines at least oneorientation of an object, in particular of the capturing device,preferably with regard to a common coordinate system, as furtherdescribed in the embodiments below. With the orientation being measuredby a tracking system, it is not necessary to constraint the orientationand/or movement of the capturing device to a certain position or to haveany prior knowledge on the orientation and/or movement of the capturingdevice. In an aspect of the invention, it is proposed to align theorientation of feature descriptors (particularly local featuredescriptors) with a certain given common coordinate system. Instead ofgaining a reproducible orientation from the intensities of neighboringpixels in the image, additional information on the orientation ofindividual pixels and/or orientation of the capturing device builds thebasis for orientation assignment to a feature. Aligning local featuredescriptors to a global orientation overcomes ambiguities resulting fromcongruent or near-congruent features with different orientations as theyare widespread in urban scenes and on man-made objects, as illustratedin FIG. 1 on the right and described in more detail below. In anembodiment of the invention, the tracking system comprises a sensorattached to the capturing device such as an inertial sensor, anaccelerometer, a gyroscope, or a compass.

In another embodiment, the tracking system comprises a mechanicaltracking system based on a physical connection between the capturingdevice and a fixed reference point, an electromagnetic tracking systemwhere magnetic fields are generated and measured, an acoustic trackingsystem working with acoustic waves, and/or an optical tracking systemusing light emitted and/or reflected from the capturing device. Saidoptical tracking system can be either integrated into the capturingdevice or be realized as an external system separately from thecapturing device.

In one embodiment of the invention, the common coordinate system is aworld coordinate system as measured with a tracking system deliveringabsolute values (e.g. compass and/or inertial sensors attached to thedevice).

In another embodiment, the common coordinate system comprises anycoordinate system relative to which a tracking system deliverstransformations. Relative changes in orientation, for instance measuredwith a gyroscope, can be accumulated to compute the absolute orientationin common coordinates at every instant without having any sensor thatmeasures the absolute orientation.

In another embodiment, the capturing device comprises a range datacapturing device, particularly a laser scanner, time-of-flight camera,or a stereo camera, which provides image pixels with an associated depthand/or 3D position.

In another aspect of the invention, the method further comprises thestep of normalizing the neighborhood of the at least one feature withrespect to the orientation in a common coordinate system. Particularly,in the step of normalizing the neighborhood of the at least one featurethe orientation provides an angle for rotating the image. Optionally, inthe step of normalizing the neighborhood of the at least one feature theorientation is used to warp the neighborhood pixels or the entire imageto one or more reference orientations to correct for perspectivedistortions in particular feature neighborhoods.

In another aspect of the invention, a method of matching features of twoor more images comprises the steps of extracting at least one firstfeature of a first image and at least one second feature of a secondimage, providing a first descriptor for the first feature and a seconddescriptor for the second feature, wherein at least one of the first andsecond descriptors is provided according to aspects of the method asdescribed above, and comparing the first and second descriptors in amatching process for the first and second features. In the matchingprocess it may then be determined based on a similarity measure whetherthe first and second features correspond with each other.

According to an embodiment of the invention, one or more directions ofthe at least one feature are computed based on pixel intensities ofneighboring pixels and stored with respect to the common coordinatesystem. In the matching stage only features with similar directions withrespect to the common coordinate system are matched to reduce the numberof comparisons needed and decrease the ratio of false matches. Accordingto embodiments of the invention, the method may be implemented in aprocess of stereo matching, particularly wide-baseline stereo matching,camera tracking, image retrieval, object recognition, visual search,pose estimation, visual surveillance, scene reconstruction, motionestimation, panorama stitching or image restoration.

In a further aspect of the invention, there is provided a computerprogram product adapted to be loaded into the internal memory of adigital computer system coupled with at least one capturing device forcapturing an image, and comprising software code sections by means ofwhich the steps according to any of the methods and embodiments asdescribed herein are performed when said product is running on saidcomputer system.

Further embodiments and aspects of the invention will be apparent fromthe dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be explained in more detail with reference to thefollowing figures in which aspects of the invention are depictedaccording to various exemplary embodiments.

FIG. 1 shows an exemplary scene in which an image is captured by amobile device having a camera on the rear side thereof, and in whichfeatures are extracted from the image and schematically shown in anormalized depiction according to a standard approach (left) andaccording to an embodiment of the invention (right),

FIG. 2 shows a standard approach to invariance to rotation in anexemplary depiction of a feature point under two different orientations,a normalization of local neighboring pixels of the feature points andthe construction of a respective descriptor,

FIG. 3 shows in a flow diagram a standard approach for constructing afeature descriptor,

FIG. 4 shows in a flow diagram an embodiment of a method according tothe invention for constructing a feature descriptor,

FIG. 5 shows in a schematic manner an exemplary first image and secondimage depicting the same static object from different viewpoints withrespective extracted features which are to be matched.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows an exemplary scene in which an image is captured by amobile device having a camera on the rear side thereof, and in whichfeatures are extracted from the image and schematically shown in anormalized depiction according to a standard approach (shown on the leftand as described above) and according to an embodiment of the invention(shown on the right).

According to the shown embodiment of the invention, again the realobject 3, which is in the present example a building having a window 4,is captured by a mobile device 2 having a camera on the rear side (notshown). For instance, the mobile device 2 may be a mobile phone ordigital camera having a microprocessor for image processing and a camerawith an optical lens on the rear side for capturing an image of thewindow 4. However, any other type of device may be used. Likewise, anyother type of system configuration with a processing device containing amicroprocessor for image processing and a camera may be used either inintegrated or distributed form.

The mobile device comprises a sensor 5, for example an inertial sensor,an accelerometer, a gyrometer and/or a compass. As such, the sensor isassociated with the camera of the mobile device as the camera is alsopart of the mobile device. The sensor is appropriate for measuring anorientation of the mobile device 2 with reference to a common coordinatesystem 10. This can be measured either in an absolute manner or byaccumulating relative orientation data over time. Commonly availablemobile phones and digital cameras are often equipped with built-indigital accelerometers and compasses that provide a measured valueindicative of the current orientation of the device. This information,along with in-formation about the intrinsic parameters of the cameraenable the transformation of any orientation in world coordinates, e.g.of the gravitational force or the north, in the coordinate system of thecamera image.

On the display 7 of the mobile device 2, the window 4 as captured by thecamera of the mobile device 2 is depicted as shown. An image processingmethod performed in a microprocessor of the mobile device 2 (or of anexternal device communicating with the mobile device 2) extractsfeatures from the captured image, for example the features F11 to F14representing the four corners of the window as rather prominent featuresof the window, and creates a feature descriptor for each of the featuresF11 to F14, as described in more detail below. The created local featuredescriptor describes these features F11 to F14 in a different way ascompared to the left column (standard approach as described above)making them clearly distinguishable, as illustrated by the extractedfeatures F11 to F14 depicted in a normalized coordinate system in theright column. Particularly, aligning the feature orientation to a globalorientation as defined by coordinate system 10 of the sensor 5 resultsin four well distinguishable descriptors without constraining the deviceorientation. Features taken under different camera orientations can bematched.

In FIG. 4, there is shown a flow diagram of an embodiment of a methodaccording to the invention for constructing a feature descriptor. Instep S11, an image is captured by a capturing device, e.g. the camera ofthe mobile device 2, or loaded from a storage medium. In step S12,feature points are extracted from the image to gain feature points in a2-dimensional description (parameters u, v). In step S13 the featureorientation is computed for the extracted feature point using spatialinformation on the orientation of the capturing device (parameters x, y,z) provided by a tracking system.

For example, the tracking system gives the orientation of the capturingdevice with respect to a world coordinate system as Euler angles andfeature descriptors are supposed to be aligned with the gravitationalforce. A very simple way to gain the orientation for all features is totransform the gravitational force to a coordinate system attached to thecapturing device using the Euler angles first and then project it ontothe image plane. Thereby, the direction of the gravitational force inthe image is computed and used for all features in the image. Thistechnique assumes orthogonal projection which is generally not the case.Incorporating the intrinsic parameters of the camera relaxes thisassumption but still all techniques based on 2D images assume everythingvisible in the image to lie on a plane and therefore are approximations.

In step S14, an orientation assignment is performed to add to theparameters u, v an orientation angle a based on the feature orientationangle a determined in step S13. Thereafter, a neighborhood normalizationstep S15 is performed to gain normalized neighborhood pixel intensitiesi[ ]. In the final step S16, a feature descriptor in the form of adescriptor vector d[ ] is created for the respective extracted featuredepending on a parameter which is indicative of an orientation of theextracted feature, particularly resulting from the orientationassignment in step S14.

According to another embodiment of the invention, as the capturingdevice a range data capturing device may be used, wherein pixels ofimages taken with any kind of range data capturing device, such as laserscanners, time-of-flight cameras, or stereo cameras may have associated3D coordinates. In this case any orientation in a common coordinatesystem for a particular feature point can be computed from the 3Dpositions of the neighboring pixels of the feature point.

Given a feature at pixel P, for all neighborhood pixels Ni (where i isthe index of the neighboring pixel), the 3D vector originating from Pand pointing to Ni is computed. The two nearest neighbors to the desiredorientation vector are determined and used to interpolate the desiredorientation in image space.

Furthermore, the knowledge of the 3D position of at least two pixelsallows for computing a rotation angle for in-plane rotation in theneighborhood normalization step. If the 3D world coordinates of three ormore pixels are known, a three-dimensional transformation can becomputed to warp the local neighborhood or the entire image to one ormore reference orientations in the normalization step.

Feature descriptors extracted from images with associated spatialinformation of different kind from different tracking systems can bematched using the proposed technique.

FIG. 1 schematically compares the results of standard approaches againstglobally-aligned local feature descriptors determined according to theinvention by the example of the four corners of a window, as shown andas described above. While the standard approach (features F1-F4 on theleft) results in four identical descriptors, the global alignment inaccordance with the invention leads to clearly distinctive featuredescriptors (features F11-F14 on the right).

Therefore, according to aspects of the invention, when extracting localfeature descriptors from images with associated information on theorientation of the capturing device with respect to a common coordinatesystem, it is proposed to assign the orientation of a feature based onthis information. Particularly, the orientation for a feature is beingaligned with a common coordinate system, projected to the imagecoordinate system. This enables higher distinctiveness betweendescriptors of congruent or near-congruent features with differentorientations while allowing for free movement and rotation of thecamera. The method according to the invention can be easily plugged intoany existing local feature descriptor that relies on a normalized localneighborhood by taking into account the measured orientation, as shownin FIG. 4.

Optionally the measured device orientation does not only influence thefeature orientation assignment in that it provides a single angle torotate the camera image about in the neighborhood normalization step,but is also used to warp parts of or the entire image to one or morereference orientations to correct for perspective distortions in theparticular feature neighborhoods in addition.

For example, the method as described above may be implemented in aprocess of stereo matching, particularly wide-baseline stereo matching,camera tracking, image retrieval, image classification, objectclassification or object recognition.

The goal of stereo matching is to reconstruct the 3D geometry of a sceneor object given two images of it taken from different viewpoints. Thisis done by finding corresponding pixels in the two images depicting thesame 3D point and computing their depths by means of triangulation.

Camera tracking describes the process of computing the pose (positionand orientation) of a camera given one or more camera images. Featuresin the camera image are either matched against reference features withknown 3D positions to compute an absolute pose or against features fromthe previous frame to compute the relative change in position andorientation.

Classification of images or objects assigns a given image orthree-dimensional description of an object to one of n possible classes.An example would be a method that tells if a given image depicts applesor pears, even if the particular fruit in the query image has not beenused for training or as a reference image. Whereas in image retrievaland object recognition for a given query the exactly matching referenceimage or object is searched for.

All these techniques rely on the matching of features of two or moreimages.

FIGS. 5A and 5B show in a schematic manner an exemplary first image andsecond image depicting the same object with respective extractedfeatures which are to be matched. For example, the images may have beencaptured under different conditions or circumstances, such as differentperspectives, light conditions, etc.

In the first image IMI, a real static object ROI as shown in FIG. 5A iscaptured by a camera (not shown). In the image IMI features of the realobject ROI are extracted, such as shown in FIG. 5A by features F51. In afollowing step, descriptors may be computed for every extracted featureF51 in accordance with the method of the invention. These features F51are then matched with features F52 extracted in the second image IM2.The second image IM2 is depicting a real object R02 which correspondswith real object ROI under a different viewpoint, wherein for thefeatures F52 also a respective descriptor is determined. Particularly,if the descriptors of features F51 and F52 are relatively close in termsof a certain similarity measure, they are matched. For example, if everydescriptor is written as a vector of numbers, when comparing twodescriptors, one can use the Euclidian distance between twocorresponding vectors as similarity measure.

There are three matches shown in FIG. 5 illustrated by connecting themwith a dashed line. Match M51 is a correct one since the two featuresmatched here correspond to the same physical entity, whereas matches M52and M53 are wrong as they match features corresponding to differentphysical entities. In match M52 the two features have a similar(near-congruent) neighborhood in different orientations. While suchmismatches are common using standard approaches, this invention aims toavoid them. The two features matched in M53 have a similar neighborhoodin a similar orientation and can therefore lead to mismatches in bothstandard approaches and an embodiment of this invention.

The common way to compute the orientation of a feature based on pixelintensities of neighboring pixels has a high repeatability and istherefore a reliable characteristic of a feature. When aligning featureswith a common coordinate system as suggested in this invention, thischaracteristic can be used to add additional distinctiveness to thedescriptors and increase matching performance. When extracting features,optionally one or more directions based on image intensities arecomputed and stored with respect to the common coordinate system foreach feature. In the matching stage the number of comparisons needed canbe reduced by comparing only features with similar directions withrespect to the common coordinate system.

This detailed description has set forth some embodiments of the presentinvention. It is to be understood that the above description of apossible implementation is intended to be illustrative and notrestrictive. Moreover, in this disclosure the terms “first”, “second”,etc., are used merely as labels, and are not intended to imposenumerical requirements on their objects. Other embodiments andmodifications within the scope of the claims will be apparent to thoseof skill in the art upon studying the above description in connectionwith the drawings.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment(s) disclosed herein as thebest mode contemplated for carrying out this invention.

What is claimed is:
 1. A method of providing a descriptor for at leastone feature of an image, comprising the steps of: providing an imagecaptured by a capturing device and extracting at least one feature fromthe image; and assigning a descriptor to the at least one feature, thedescriptor depending on at least one parameter which is indicative of anorientation in the image with respect to a projection of the directionof the gravitational force in the image, wherein the at least oneparameter is determined from at least one tracking system; and whereinthe at least one parameter is determined under consideration ofintrinsic parameters of the capturing device.
 2. The method of claim 1,wherein the at least one parameter is determined from an orientation ofthe capturing device in reference to a common coordinate system.
 3. Themethod of claim 1, wherein the tracking system comprises a sensor, inparticular an inertial sensor, or an accelerometer, or a gyroscope or acompass, which is associated with the capturing device.
 4. The method ofclaim 1, wherein the tracking system comprises a mechanical and/orelectromagnetic and/or acoustic and/or optical tracking system.
 5. Themethod of claim 1, wherein the capturing device comprises a range datacapturing device, particularly a laser scanner, time-of-flight camera,or a stereo camera, which provides image pixels with an associated depthand/or 3D position.
 6. The method of claim 1, wherein the at least oneparameter is determined along with information about intrinsicparameters of the capturing device for a transformation in a coordinatesystem of the image.
 7. The method of claim 1, wherein for the at leastone extracted feature the at least one parameter is determined from 3Dpositions of pixels of the image.
 8. The method of claim 1, furthercomprising the step of normalizing the neighborhood of the at least onefeature with respect to the orientation.
 9. The method of claim 8,wherein in the step of normalizing the neighborhood of the at least onefeature the orientation provides an angle for rotating the image. 10.The method of claim 8, wherein in the step of normalizing theneighborhood of the at least one feature the orientation is used to warpthe entire image or parts of it to one or more reference orientations tocorrect for perspective distortions in particular feature neighborhoods.11. The method of claim 1, where one or more directions of the featurewith respect to the orientation in a common coordinate system arecomputed based on pixel intensities and stored as part of thedescriptor.
 12. A method of matching features of two or more images,comprising: extracting at least one first feature of a first image andat least one second feature of a second image; providing a firstdescriptor for the first feature and a second descriptor for the secondfeature, wherein at least one of the first and second descriptors isprovided according to the method of claim 1; and comparing the first andsecond descriptors in a matching process for the first and secondfeatures.
 13. The method of claim 12, wherein in the matching process itis determined based on a similarity measure whether the first and secondfeatures correspond with each other.
 14. The method of claim 13 wherethe similarity measure comprises a multi-stage method that successivelycomputes different measures to reject wrong matches as early aspossible.
 15. The method of claim 12, wherein the method is implementedin a process of stereo matching, particularly wide-baseline stereomatching, camera tracking, image retrieval, object recognition, visualsearch, pose estimation, visual surveillance, scene reconstruction,motion estimation, panorama stitching or image restoration.
 16. Themethod of claim 1, wherein the method is implemented in a process ofstereo matching, particularly wide-baseline stereo matching, cameratracking, image retrieval, object recognition, visual search, poseestimation, visual surveillance, scene reconstruction, motionestimation, panorama stitching or image restoration.
 17. Anon-transitory computer readable medium comprising software codesections adapted to be loaded into the internal memory of a digitalcomputer system coupled with at least one capturing device for capturingan image or a memory medium for loading an image therefrom by means ofwhich the steps according to claim 1 are performed when running on saidcomputer system.
 18. The method according to claim 1, wherein theorientation is aligned with the projection of the direction of thegravitational force in the image.